Summary

Row

confirmed

3,755,341

active

2,246,097 (59.8%)

recovered

1,245,413 (33.2%)

death

263,831 (7%)

Row

Cases Distribution by Type (Top 25 Countries)

Row

Daily Cumulative Cases by Type

Recovery and Death Rates by Country

Map

Map

Data

About

The Coronavirus Dashboard

Last updated: 06 May

This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available here

Data

The input data for this dashboard is the coronavirus R package (dev version). The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository

Packages

Deployment and reproducibly

The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:

For any question or feedback, you can either open an issue or contact me on Twitter.

Contribution

The Map tab was contributed by Art Steinmetz on this pull request. Thanks Art!

---
title: "Coronavirus"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(leaflet)
library(leafpop)
library(purrr)

library(coronavirus)
library(covid19italy)

data(coronavirus)
data(italy_total)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>% 
  # dplyr::filter(date == max(date)) %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(names_from =  type, 
                     values_from = total) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>% 
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(names_from = type,
                     values_from = total) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active =  confirmed - death - recovered) %>%
  dplyr::mutate(confirmed_cum = cumsum(confirmed),
                death_cum = cumsum(death),
                recovered_cum = cumsum(recovered),
                active_cum = cumsum(active))
  

df1 <- coronavirus %>% dplyr::filter(date == max(date))


#------------trajectory plot data prep------------

df_china <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "China") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(china = cumsum(cases)) %>%
  dplyr::filter(china > 100)  %>%
  dplyr::select(-cases, -date)
df_china$index <- 1:nrow(df_china)


df_uk <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "United Kingdom") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(uk = cumsum(cases)) %>%
  dplyr::filter(uk > 100)  %>%
  dplyr::select(-cases, -date)
df_uk$index <- 1:nrow(df_uk)


df_fr <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "France") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(france = cumsum(cases)) %>%
  dplyr::filter(france > 100)  %>%
  dplyr::select(-cases, -date)
df_fr$index <- 1:nrow(df_fr)

df_us <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "US") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(us = cumsum(cases)) %>%
  dplyr::filter(us > 100)  %>%
  dplyr::select(-cases, -date)
df_us$index <- 1:nrow(df_us)

df_iran <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Iran") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(iran = cumsum(cases)) %>%
  dplyr::filter(iran > 100)  %>%
  dplyr::select(-cases, -date)
df_iran$index <- 1:nrow(df_iran)

df_sk <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Korea, South") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(sk = cumsum(cases)) %>%
  dplyr::filter(sk > 100)  %>%
  dplyr::select(-cases, -date)
df_sk$index <- 1:nrow(df_sk)

df_spain <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Spain") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(spain = cumsum(cases)) %>%
  dplyr::filter(spain > 100)  %>%
  dplyr::select(-cases, -date)
df_spain$index <- 1:nrow(df_spain)



df_italy <- italy_total %>% dplyr::select(date, italy = cumulative_cases) %>%
  dplyr::filter(italy > 100) %>%
  dplyr::select(-date)
df_italy$index <- 1:nrow(df_italy)

df_trajectory <- df_china %>% 
  dplyr::left_join(df_italy, by = "index") %>%
  dplyr::left_join(df_iran, by = "index") %>%
  dplyr::left_join(df_sk, by = "index") %>%
  dplyr::left_join(df_us, by = "index") %>%
  dplyr::left_join(df_fr, by = "index") %>%
  dplyr::left_join(df_uk, by = "index") %>%
  dplyr::left_join(df_spain, by = "index")



```


Summary
=======================================================================
Row
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "), 
         caption = "Total Confirmed Cases", 
         icon = "fas fa-user-md", 
         color = confirmed_color)
```


### active {.value-box}

```{r}
valueBox(value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1), 
                       "%)", sep = ""), 
         caption = "Active Cases", icon = "fas fa-ambulance", 
         color = active_color)
```

### recovered {.value-box}

```{r}
valueBox(value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1), 
                       "%)", sep = ""), 
         caption = "Recovered Cases", icon = "fas fa-heartbeat", 
         color = recovered_color)
```

### death {.value-box}

```{r}

valueBox(value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-heart-broken", 
         color = death_color)
```


Row
-----------------------------------------------------------------------

### Cases Distribution by Type (Top 25 Countries) 

```{r daily_summary}


plotly::plot_ly(data = df[1:30,], 
                x = ~ country, 
                y = ~ unrecovered, 
                # text =  ~ confirmed, 
                # textposition = 'auto',
                type = "bar", 
                name = "Active",
                marker = list(color = active_color)) %>%
  plotly::add_trace(y = ~ recovered, 
                    # text =  ~ recovered, 
                    # textposition = 'auto',
                    name = "Recovered",
                    marker = list(color = recovered_color)) %>%
  plotly::add_trace(y = ~ death, 
                    # text =  ~ death, 
                    # textposition = 'auto',
                    name = "Death",
                    marker = list(color = death_color)) %>%
  plotly::layout(title = "",
                 barmode = 'stack',
                 yaxis = list(title = "Total Cases (log scaled)",
                              type = "log"),
                 xaxis = list(title = paste("Last update:", format(max(coronavirus::coronavirus$date), '%d %B'), sep = " ")),
                 hovermode = "compare",
                 annotations = list(
                   text = paste("Last update:", format(max(coronavirus::coronavirus$date), '%d %B'), sep = " "),
                   xref = "paper",
                   yref = "paper",
                   showarrow = FALSE,
                  x = 0.95,
                  y = 1
                 ),
                 margin =  list(
                   # l = 60,
                   # r = 40,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))





```

Row {data-width=400}
-----------------------------------------------------------------------


### Daily Cumulative Cases by Type
    
```{r}

# plotly::plot_ly(df_daily, x = ~date, y = ~active_cum, name = 'Active', type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = "#1f77b4") %>%
# plotly::add_trace(y = ~recovered_cum, name = 'Recovered', fillcolor = "green") %>%
# plotly::add_trace(y = ~death_cum, name = "Death", fillcolor = "red") %>%
#   plotly::layout(title = "",
#          xaxis = list(title = "",
#                       showgrid = FALSE),
#          yaxis = list(title = "Cumulative Number of Cases",
#                       showgrid = FALSE),
#          legend = list(x = 0.1, y = 0.9),
#                  hovermode = "compare")


plotly::plot_ly(data = df_daily,
                x = ~ date,
                y = ~ active_cum, 
                name = 'Active', 
                fillcolor = active_color,
                type = 'scatter',
                mode = 'none', 
                stackgroup = 'one') %>%
  plotly::add_trace(y = ~ recovered_cum,
                    name = "Recovered",
                    fillcolor = recovered_color) %>%
  plotly::add_trace(y = ~ death_cum,
                    name = "Death",
                    fillcolor = death_color) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
  

```


### Recovery and Death Rates by Country
    
```{r}
df_summary <-coronavirus %>% 
  # dplyr::filter(Country.Region != "Others") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 25) %>%
  dplyr::select(country = Country.Region, confirmed, recovered, death) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
         death_rate = death / confirmed)  
df_summary %>%
  DT::datatable(rownames = FALSE,
            colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
            options = list(pageLength = nrow(df_summary), dom = 'tip')) %>%
  DT::formatPercentage("recover_rate", 2) %>%
  DT::formatPercentage("death_rate", 2) 
```


Map
=======================================================================

**Map**

```{r map}
# map tab added by Art Steinmetz

cv_data_for_plot <- coronavirus %>% 
  dplyr::filter(cases > 0) %>% 
  dplyr::group_by(Country.Region,Province.State,Lat,Long,type) %>% 
  dplyr::summarise(cases = sum(cases)) %>% 
  dplyr::mutate(log_cases = 2 * log(cases)) %>% 
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red","green"), domain = c("confirmed", "death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk( function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(data=cv_data_for_plot.split[[df]],
                 lng=~Long, lat=~Lat,
#                 label=~as.character(cases),
                 color = ~pal(type),
                 stroke = FALSE,
                 fillOpacity = 0.8,
                 radius = ~log_cases,
                 # popup =  leafpop::popupTable(cv_data_for_plot.split[[df]],
                 popup =  popupTable(cv_data_for_plot.split[[df]],
                                              feature.id = FALSE,
                                              row.numbers = FALSE,
                                              zcol=c("type","cases","Country.Region","Province.State")),
                 group = df,
#                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
                 labelOptions = labelOptions(noHide = F,
                                             direction = 'auto'))
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE) 
  )
```

Trends
=======================================================================


Column {data-width=400}
-------------------------------------
    
### New Cases - Top 15 Countries (`r  max(coronavirus$date)`)
    
```{r new_cases}
max_date <- max(coronavirus$date)
coronavirus %>% 
  dplyr::filter(type == "confirmed", date == max_date) %>%
  dplyr::group_by(Country.Region) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  dplyr::arrange(-total_cases) %>%
  dplyr::mutate(country = factor(Country.Region, levels = Country.Region)) %>%
  dplyr::ungroup() %>%
  dplyr::top_n(n = 15, wt = total_cases) %>%
  plotly::plot_ly(x = ~ country,
                  y = ~ total_cases,
                  text = ~ total_cases,
                  textposition = 'auto',
                  type = "bar") %>%
  plotly::layout(yaxis = list(title = "Number of Cases"),
                 xaxis = list(title = ""),
                 margin =  list(
                   l = 10,
                   r = 10,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))

```


### Trajectory Plot - Major Countries 

```{r}
plotly::plot_ly(data = df_trajectory) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ china,
                    name = "China",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ italy,
                    line = list(color = "red", width = 2),
                    name = "Italy") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ us,
                    name = "United States",  line = list(width = 2)) %>%
    plotly::add_lines(x = ~ index,
                    y = ~ uk,
                    name = "United Kingdom",  line = list(width = 2)) %>%
      plotly::add_lines(x = ~ index,
                    y = ~ france,
                    name = "France",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ iran,
                    name = "Iran",  line = list(color = "orange", width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ sk,
                    name = "South Korea",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ spain,
                    name = "Spain") %>%
  plotly::layout(yaxis = list(title = "Cumulative Positive Cases",type = "log"),
                 xaxis = list(title = "Days since the total positive cases surpass 100"),
                 legend = list(x = 0.7, y = 0.3),
                 hovermode = "compare")
```
   
Column {data-width=600}
-------------------------------------
   
### Recovery and Death Rates for Countries with at Least 10000 Cases

```{r}
coronavirus::coronavirus %>% 
  # dplyr::filter(Country.Region != "Others") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 10000) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
                death_rate = death / confirmed) %>% 
  dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
                death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
  plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
                  x = ~ round(100 * death_rate, 1),
                  size = ~  log(confirmed),
                  sizes = c(5, 70),
                  type = 'scatter', mode = 'markers',
                  color = ~ Country.Region,
                  marker = list(sizemode = 'diameter' , opacity = 0.5),
                  hoverinfo = 'text',
                  text = ~paste("
", Country.Region, "
Confirmed Cases: ", confirmed, "
Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""), "
Death Rate: ", paste(round(100 * death_rate, 1), "%", sep = "")) ) %>% plotly::layout(yaxis = list(title = "Recovery Rate", ticksuffix = "%"), xaxis = list(title = "Death Rate", ticksuffix = "%", dtick = 1, tick0 = 0), hovermode = "compare") ``` ### Cases Status Update for `r max(coronavirus$date)` ```{r} daily_summary <- coronavirus %>% dplyr::filter(date == max(date)) %>% dplyr::group_by(Country.Region, type) %>% dplyr::summarise(total = sum(cases)) %>% tidyr::pivot_wider(names_from = type, values_from = total) %>% dplyr::arrange(-confirmed) %>% dplyr::select(country = Country.Region, confirmed, recovered, death) DT::datatable(data = daily_summary, rownames = FALSE, colnames = c("Country", "Confirmed", "Recovered", "Death"), options = list(pageLength = nrow(daily_summary), dom = 'tip')) ``` Data ======================================================================= ```{r} coronavirus %>% dplyr::select(Date = date, Province = Province.State, Country = Country.Region, `Case Type` = type, `Number of Cases` = cases) %>% DT::datatable(rownames = FALSE, options = list(searchHighlight = TRUE, pageLength = 20), filter = 'top') ``` About ======================================================================= **The Coronavirus Dashboard** Last updated: `r format(max(coronavirus::coronavirus$date), '%d %B')` This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available [here](https://github.com/RamiKrispin/coronavirus_dashboard) **Data** The input data for this dashboard is the [coronavirus](https://github.com/RamiKrispin/coronavirus) R package (dev version). The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv) **Packages** * Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package. * Visualization - the [plotly](https://plot.ly/r/) package for the plots and [leaflet](https://rstudio.github.io/leaflet/) for the map * Data manipulation - [dplyr](https://dplyr.tidyverse.org/), and [tidyr](https://tidyr.tidyverse.org/) * Tables - the [DT](https://rstudio.github.io/DT/) package **Deployment and reproducibly** The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps: * Fork the dashboard [repository](https://github.com/RamiKrispin/coronavirus_dashboard), or * Clone it and push it to your Github package * Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example) For any question or feedback, you can either open an [issue](https://github.com/RamiKrispin/coronavirus_dashboard/issues) or contact me on [Twitter](https://twitter.com/Rami_Krispin). **Contribution** The **Map** tab was contributed by [Art Steinmetz](@adababbage) on this [pull request](https://github.com/RamiKrispin/coronavirus_dashboard/pull/1). Thanks Art!